Stock Market Prediction
32 papers with code • 3 benchmarks • 3 datasets
In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from twitter and analyze the correlation between stock market movements of a company and sentiments in tweets.
In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies.
A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values.
Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy.
Forecasting directional movements of stock prices for intraday trading using LSTM and random forests
Hence we outperform the single-feature setting in Fischer & Krauss (2018) and Krauss et al. (2017) consisting only of the daily returns with respect to the closing prices, having corresponding daily returns of 0. 41% and of 0. 39% with respect to LSTM and random forests, respectively.
Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments.
In this work, we present our findings and experiments for stock-market prediction using various textual sentiment analysis tools, such as mood analysis and event extraction, as well as prediction models, such as LSTMs and specific convolutional architectures.